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A competitive particle swarm optimization for nonlinear first arrival traveltime tomography

Abstract : Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, common global optimization techniques such as Genetic Algorithm (GA) or Simulated Annealing (SA) are insensitive to the initial model but are computationally expensive and require many controlling parameters. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to allow “worst” particles to explore the model parameter space and eventually find a better minimum. A tomography algorithm based on CPSO is successfully applied on a 3D synthetic case corresponding to a typical calibration shot geometry in a hydraulic fracturing context.
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Contributor : Mark Noble <>
Submitted on : Thursday, November 17, 2016 - 11:54:50 AM
Last modification on : Wednesday, October 14, 2020 - 3:52:17 AM



Keurfon Luu, Mark Noble, Alexandrine Gesret. A competitive particle swarm optimization for nonlinear first arrival traveltime tomography. 2016 Society of Exploration Geophysicists annual meeting, 2016, Dallas, United States. pp. 2740-2744, ⟨10.1190/segam2016-13840267.1⟩. ⟨hal-01398464⟩



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